Search Results for author:
Found 10 papers, 3 papers with code
Aphid Cluster Recognition and Detection in the Wild Using Deep Learning Models
To facilitate the use of machine learning models, we further process the images by cropping them into patches, resulting in a labeled dataset comprising 151, 380 image patches.
On the Real-Time Semantic Segmentation of Aphid Clusters in the Wild
We have collected and labeled a large aphid image dataset in the field, and propose the use of real-time semantic segmentation models to segment clusters of aphids.
A New Dataset and Comparative Study for Aphid Cluster Detection
Aphids are one of the main threats to crops, rural families, and global food security.
Gender, Smoking History and Age Prediction from Laryngeal Images
In this study, we made the first endeavor to employ deep learning models to predict patient demographic information to improve detector model performance.
Dynamic Label Assignment for Object Detection by Combining Predicted IoUs and Anchor IoUs
For anchor-based detection models, the IoU (Intersection over Union) threshold between the anchors and their corresponding ground truth bounding boxes is the key element since the positive samples and negative samples are divided by the IoU threshold.
An Unsupervised Domain Adaptation Model based on Dual-module Adversarial Training
In this paper, we propose a dual-module network architecture that employs a domain discriminative feature module to encourage the domain invariant feature module to learn more domain invariant features.
Miti-DETR: Object Detection based on Transformers with Mitigatory Self-Attention Convergence
Object Detection with Transformers (DETR) and related works reach or even surpass the highly-optimized Faster-RCNN baseline with self-attention network architectures.
Colonoscopy Polyp Detection and Classification: Dataset Creation and Comparative Evaluations
This work can serve as a baseline for future research in polyp detection and classification.
Six-channel Image Representation for Cross-domain Object Detection
If we train the detector using the data from one domain, it cannot perform well on the data from another domain due to domain shift, which is one of the big challenges of most object detection models.
Efficient Golf Ball Detection and Tracking Based on Convolutional Neural Networks and Kalman Filter
The detection is performed on small image patches instead of the entire image to increase the performance of small ball detection.
